No Cover Image

Journal article 465 views 68 downloads

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

Yimin Qu, Yuanyuan Zhuo, Jack Lee, Xingxian Huang, Zhuoxin Yang, Haibo Yu, Jinwen Zhang, Weiqu Yuan, Jiaman Wu, David Owens Orcid Logo, Benny Zee

Frontiers in Neurology, Volume: 13

Swansea University Author: David Owens Orcid Logo

  • 60879_VoR.pdf

    PDF | Version of Record

    © 2022 Qu, Zhuo, Lee, Huang, Yang, Yu, Zhang, Yuan, Wu, Owens and Zee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)

    Download (1.22MB)

Abstract

Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as ea...

Full description

Published in: Frontiers in Neurology
ISSN: 1664-2295
Published: Frontiers Media SA 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60879
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study design: A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results: 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).Conclusion: A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
College: Faculty of Medicine, Health and Life Sciences
Funders: This study was supported by the General Research Fund (GRF) of the Hong Kong Research Grant Council (No.14139116); National Natural Science Foundation of China (No.81803952); Science Technology and Innovation Commission of Shenzhen Municipality (KCXFZ20201221173208024).